TY - JOUR
T1 - Spectral fluorescence signatures and partial least squares regression
T2 - Model to predict dissolved organic carbon in water
AU - Marhaba, Taha F.
AU - Bengraïne, Karim
AU - Pu, Yong
AU - Aragó, Jaime
N1 - Funding Information:
This work was funded in part by New Jersey Department of Environmental Protection (NJDEP). The authors thank Dr. R. Lee Lippincott of NJDEP Division of Science, Research and Technology for his valuable support, Philip Roosa of Passaic Valley Water Commission and Oleg Kostin of Elizabethtown water company.
PY - 2003/2/28
Y1 - 2003/2/28
N2 - Spectro-fluorescence signature (SFS) of water samples contains information that may be used to quantify dissolved organic carbon (DOC) if combined with multivariate analyses. A model was built through SFS and partial least squared (PLS) regression. The SFSs of 219 samples of natural water along the Raritan River and Millstone River watersheds located in central New Jersey, and their corresponding DOC concentrations were used to build the model. Calibration, full cross-validation, and prediction performances of various models were statistically compared before optimal model selection. The final selected model, tested on the Passaic River watershed in northern New Jersey, provided a bias of 0.028mg/l and a root mean squared error of prediction (RMSEP) of 0.35mg/l. Linked to PLS, SFS can be a quality and cost effective method to perform on-line rapid DOC measurements.
AB - Spectro-fluorescence signature (SFS) of water samples contains information that may be used to quantify dissolved organic carbon (DOC) if combined with multivariate analyses. A model was built through SFS and partial least squared (PLS) regression. The SFSs of 219 samples of natural water along the Raritan River and Millstone River watersheds located in central New Jersey, and their corresponding DOC concentrations were used to build the model. Calibration, full cross-validation, and prediction performances of various models were statistically compared before optimal model selection. The final selected model, tested on the Passaic River watershed in northern New Jersey, provided a bias of 0.028mg/l and a root mean squared error of prediction (RMSEP) of 0.35mg/l. Linked to PLS, SFS can be a quality and cost effective method to perform on-line rapid DOC measurements.
KW - Dissolved organic carbon (DOC)
KW - New Jersey
KW - Partial least squared regression (PLS)
KW - Spectrofluorescence signature (SFS)
KW - Watershed
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U2 - 10.1016/S0304-3894(02)00246-7
DO - 10.1016/S0304-3894(02)00246-7
M3 - Article
C2 - 12573831
AN - SCOPUS:0037469954
SN - 0304-3894
VL - 97
SP - 83
EP - 97
JO - Journal of Hazardous Materials
JF - Journal of Hazardous Materials
IS - 1-3
ER -